SCADA and PMU Measurement Based Methods for Robust Hybrid State Estimation

In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of measurements from supervisory control and data acquisition (SCADA) system and phasor measurement unit (PMU) are quite different, causing time skewness problem. In this paper, we propose a robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue. In the framework, robust Mahalanbis distances are proposed to detect system abnormalities and assign appropriate weights to each chosen buffered PMU measurements. Those weights are further utilized by the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator to filter out non-Gaussian PMU measurement noise and help suppress outliers. In the meantime, the SHGM estimator is used to handle unknown noise of the received SCADA measurements, yielding another set of state estimates. We show that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution. This nice property allows us to obtain the optimal state estimates by resorting to the data fusion theory for the fusion of the estimation results from two independent SHGM estimators. Extensive simulation results carried out on the IEEE 14, 30 and 118-bus test systems demonstrate the effectiveness and robustness of the proposed method.

This paper proposes a new Multi Area State Estimation (MASE) approach which utilizes all the field measurements received from the Remote Terminal Units (RTUs) of the Supervisory Control and Data Acquisition (SCADA) system and the Phasor Measurement Units (PMUs) of the Wide Area Monitoring System (WAMS). Under this approach, a power system is divided into overlapping sub-areas. Each sub-area runs its own State Estimation (SE) independently and in parallel in such a way that the MASE of the complete power system completes before the arrival of the next measurement set. During the time gap between two successive RTUs measurement sets, the PMUs measurements along with the SE results of the sub-areas, time adjusted using state forecasting technique, are utilized to run the linear SE to provide fast update of the state estimates. The effectiveness of the proposed method has been demonstrated on IEEE30 bus system and 246-Bus reduced Northern Regional Power Grid (NRPG) Indian system.

Hybrid measurement of the phasor measurement units (PMU) and the supervisory control and data acquisition (SCADA) system is used for state estimation of power transmission system when PMU deployment can not cover all buses. The traditional iterative solution process of state estimation is low efficient and may cause truncation error. A fast state estimation method based on pseudo-measurement modeling of extreme learning machine (ELM) for power transmission system state estimation is proposed. The injected powers measured by SCADA are used as input, the real and imaginary parts of bus voltages are used as output. The pseudo-measurement value model and pseudo-measurement error model are trained using historical data. In the new time, the pseudo-measurements obtained from the trained model are combined with the PMU measurements for fast linear state estimation. The final state estimation results can be obtained quickly. Simulation results of IEEE 14 system show that the proposed method not only improves the accuracy of state estimation, but also greatly reduces the calculation time. It can provide basic information for other applications in EMS accurately and quickly.

Wide Area Monitoring System (WAMS) with Phasor Measurement Unit (PMU) has the capability to do real time monitoring including fast system condition changes. On the other hand, Supervisory Control and Data Acquisition (SCADA), which is vulnerable to that condition, are still widely used in wide world power system because of its capability and maturity. This paper proposes the framework in transforming SCADA into WAMS by installing required additional PMU while keeping the conventional measurement from SCADA. Moreover, the real time state estimation procedure in managing the initial state variable and weighting factor re-weight strategy is also proposed. The result shows that the proposed WAMS hybrid warrants better estimation accuracy and faster state estimation process than SCADA. In addition, the voltage stability monitoring as WAMS application can also successfully be obtained.

SummaryIt is well known that measurements from phasor measurement unit (PMU) or supervisory control and data acquisition (SCADA) are not generally independent. Since the correlation of measurement error is a very representative feature of the actual measurement system, traditional assumptions on error independency are not adequate. In this paper, taking the correlation of measurement error of both PMU and SCADA measurements into consideration, a novel correlated extended Kalman filter (CEKF) is proposed. The actual measurement configurations are analyzed with the consideration of measurement error transfer characteristics. Then, the modified measurement error covariance matrix is calculated by using the point estimation method, which will replace the traditional diagonal variance matrix. At last, IEEE 14‐bus system and 57‐bus system are provided to illustrate the effectiveness and superiority of the method, respectively.

Nowadays, the increase in electrical energy consumption and power system restructuring have posed new challenges to the operation, control, and monitoring of power systems. In this situation, the supervisory control and data acquisition (SCADA) system is not enough to ensure power system security and stability. The SCADA is often unable to measure data of all buses simultaneously. In addition, the sampling rate in this system is not enough for some power system applications. Therefore, the information obtained from SCADA does not show power system dynamics properly. In order to improve the power system monitoring, wide area measurement system (WAMS) has been developed to overcome the problems of SCADA system. Phasor measurements units (PMUs) are the main part of WAMS system and it basically consists of three essential processes including collecting, transmitting, and analyzing data. WAMS receives obtained data via a high speed communication links. After data processing and extracting appropriate information, decisions are made to improve the power system performance. Efficient use of power system data to achieve a secure operation strategy is targeted using the WAMS system. Due to the effective role of WAMS and PMUs in the reliable operation of power system, it is necessary to study their concept and applications in this chapter. The history of PMU and its structure is presented in this chapter. In addition, the necessity of WAMS for future power system and its difference from SCADA system have been investigated in this chapter. Different algorithms and application of the WAMS are also introduced in this chapter which can be implemented to improve the performance of the future power system.

Accurate real-time states provided by the state estimator are critical for power system reliable operation and control. This paper proposes a novel phasor measurement unit (PMU)-based robust state estimation method (PRSEM) to real-time monitor a power system under different operation conditions. To be specific, an adaptive weight assignment function to dynamically adjust the measurement weight based on the distance of big unwanted disturbances from the PMU measurements is proposed to increase algorithm robustness. Furthermore, a statistical test-based interpolation matrix ${H}$ updating judgment strategy is proposed. The processed and resynced PMU information are used as priori information and incorporated to the modified weighted least square estimation to address the measurements imperfect synchronization between supervisory control and data acquisition and PMU measurements. Finally, the innovation analysis-based bad data (BD) detection method, which can handle the smearing effect and critical measurement errors, is presented. We evaluate PRSEM by using IEEE benchmark test systems and a realistic utility system. The numerical results indicate that, in short computation time, PRSEM can effectively track the system real-time states with good robustness and can address several kinds of BD.

Fault recorders can track the transients in the power system during a fault. However, they cannot provide a wide area picture of the power system operating condition. The provision to track the transients is of great interest for the power system operators. In this paper, a two-stage state estimator (SE), based on both conventional and phasor measurement unit (PMU) measurements, is presented. The estimator is able to track the dynamics of the power system states during a fault. The advantage of the proposed SE is that it does not require full power system observability by the PMU measurements. The two-stage SE was successfully tested on the IEEE 14 and 118 bus systems.

This paper presents a novel coordinated algorithm, Fast Distributed State Estimation with Linear Coordination (FDSELC), considering Phasor Measurement Units (PMUs) measurements at both subsystem and coordination levels. At the subsystem level, the estimators apply augmented Fast Decoupled State Estimation algorithm to reduce the computational time at the first step. At the coordination level, linear estimators based on PMUs measurements are able to coordinate the voltage states of boundary buses, which lead to the further improvement of computational performance. Tests on the IEEE 118-bus system demonstrate the performance of the novel FDSELC algorithms. The results are compared with centralized and other distributed state estimation algorithms in terms of both the estimation quality and computational cost. In addition, the proposed FDSELC algorithm with the distributed control architecture is suitable for future smart grids. In this situation, a large power grid can be decomposed into subsystems, and those subsystems can be estimated, monitored and controlled separately.

A wide-area measurement system (WAMS) consists of advanced measurementtechnology,thelatestcommunicationnetworkinfrastructure,andintegratedoperationalframework. The supervisory control and data acquisition (SCADA) infrastructure forenergy-management system (EMS) has been widely used in power systems for a longtime. Some of the functionalities of an EMS are system state monitoring, tie-line biascontrol,andeconomicdispatch[1].However,inrecentyears,variousdeficienciesoftheexisting SCADA-based EMS (such as quasi-steady-state calculation, non-synchronizeddataacquisition,andrelativelylowdatatransmissionrate)havebeenpointedout.Thesedefects make it impossible to sample the global state of a power system in real time.As more and more wide-area blackouts are reported, it is clear that acquiring real-timeor wide-area state information would be needed in the future. The state informationin terms of phasors of voltages and currents from a distributed wide area in real timeis therefore critical for avoiding large-area disturbances by effecting wide-area controlbased on wide-area measurements.The main enabler of WAMS is phasor measurement unit (PMU) technology. Withthe innovation of PMU, the problem of measuring the phasor quantities simultaneouslyfromawideareaofdistributedsubstations,alsocalled‘synchrophasor’,hasbeensolved.At present, the PMU technology is one of the essential enablers for WAMS. It utilizesthe availability of high-precision synchronized clock sources – extracted from globalpositioning system (GPS) receivers and samples the instantaneous analogue – quanti-ties of voltage and current magnitudes and phase angles. Many utilities are installingPMUs at very high voltage substations, but the trend is that eventually PMUs will beused to monitor all high-voltage transmission lines. There are debates as to whether thewide-area or long-distance communications that communicate phasor data, or controlaction messages, should only be done via proprietary networks, or the existing Inter-net infrastructure with quality-of-service (QoS) differentiated delivery could be used.These questions require further simulation studies as well as financial and policy deci-sions which are not considered in this chapter. We concentrate mostly on evaluatingthe network architecture, the specific network medium and technology, the appropriate

The advent of microgrids and increased utilization of renewable energy sources (RESs) contributed to the uncertainty of power generation in hybrid power systems (HPSs), which necessitated stringent monitoring and control of grid conditions. The large power swings in RESs cause significant deviations in frequency that can disrupt the stability of the system in case timely control actions are not initiated. Therefore, frequency control is of prime importance and has been handled by different levels of controllers in power systems. Data acquisition plays a major role in frequency control of HPSs for performing real-time control. In this context, supervisory control and data acquisition (SCADA) systems and phasor measurement units (PMUs) are studied based on their dynamic performance in real-time data acquisition. Additionally, the role of PMUs in frequency stability has been reviewed and the challenges faced in enabling PMUs for frequency control of HPSs has been presented.

Aiming at the problem that the current measurements provided by phasor measurement unit (PMU) is difficult to introduce to traditional state estimation, a new method to solve the compatibility problems of PMU and supervisory control and data acquisition (SCADA) is presented in this paper. By the rotated-pseudo-measurement (RPM) method, current measurements are transformed into the pseudo measurements and decoupled in polar coordinates, so that they could taken into nonlinear state estimation. Then the RPM method is simulated compared with the current transformation (CT) method. According to the result, it is showed that the RPM method has many advantages compared with the CT, such as independence of other measurements, small error in propagation and more significant in improving estimation accuracy. Moreover, the RPM method has the compatibility with traditional nonlinear state estimation and the fast calculating speed, which have good prospects of practice.

Recently, owing to the high penetration of renewable power generations, random access to dynamical loads and wide application of power-electronic devices, the modern power systems exhibit new features such as high dynamics, low inertia and weak voltage and frequency support. As a powerful tool in revealing the dynamical evolution of the modern power systems, the dynamic state estimation (DSE) scheme gains a surge of research enthusiasms. Note that in actual power systems, various styles of power generations and mixed measurements generated by the supervisory control and data acquisition (SCADA) system and the phasor measurement unit (PMU) pose higher demands on the DSE. In addition, the measurements with anomalies such as gross errors, outliers and incomplete information are often encountered due to the unreliable measurement facilities, constrained communication resources and cyber-threats. Such anomalies, if not tactfully handled, would largely impair the DSE performance. In this paper, a systematic and timely review with respect to DSE for power systems is provided. First, several typical models for power systems include quasi-steady model, synchronous-generator-based multi-machine model and renewable power generation model are introduced. Then the measurement facilities of power systems and their features as well as the measurements with anomalies are reviewed, respectively. Subsequently, the DSE schemes are recalled and discussed from the perspectives of framework, methodology, anomaly-resistant ability and application, respectively. Finally, some possible future research topics are outlined for the DSE of power systems.

Abstract—We present an analysis of a heuristic for abruptchange detection of systems with bounded state variations. Theproposed analysis is based on the Singular Value Decomposition(SVD) of a history matrix built from system observations. Weshow that monitoring the largest singular value of the historymatrix can be used as a heuristic for detecting abrupt changesin the system outputs. We provide sufficient detectability condi-tions for the proposed heuristic. As an application, we considerdetecting malicious cyber data attacks on power systems andtest our proposed heuristic on the IEEE 39-bus testbed. I. I NTRODUCTION Fault detection and supervisory control are essential toensure that a dynamical system is operating in normalconditions. These monitoring mechanisms are of higherimportance for critical systems such as power systems. Anypropagation of faults in a power system may have severeconsequences in the electricity generation, transmission, ordistribution. To this end, Supervisory Control and DataAcquisition (SCADA) systems are designed for controllingand monitoring different parts of a power grid. Traditionally,within SCADA or other conventional supervisory control andmonitoring centers, the state of the system under study isestimated at every sample time. The condition of the systemis then tested by monitoring a metric based on the estimatedstate. An abrupt change in that metric is an indicator of theoccurrence of some malfunctioning in the system dynamics.A. Designated Data AttacksIn power systems, as an example, changes of the systemdynamics have been traditionally considered as a result ofmeter aging and malfunctioning, electrical breakdown, ornatural causes such as storm, lightening, etc. However, suchchanges might be the result of a designated cyber data attackto the system. In particular, with the emergence of smart gridsand its smart hardware and software components such assmart meters, Phasor Measurement Units (PMUs), intelligent

WAMS (Wide Area Measurement System) is a newly developed technology, which can provide a reliable guarantee to the simultaneous measurement of power system. But PMU (Phasor Measurement Unit) can not be widely used in power grid now, so it is impossible that WAMS can replace SCADA (Supervisory Control And Data Acquisition) system presently. Hence, how to incorporate WAMS measuring data into other systems including SCADA has become a hot research topic currently especially the data exchange between them. In order to solve this problem, a CIM (Common Information Model)-based information model for WAMS is proposed in this paper according both to IEC 61970: EMS-API (Energy Management System Application Program Interface) part 303 and the principle and the application of WAMS. This paper establishes a new package named WAMS in the framework of CIM and then some extended classes and their attributes along with their relationships are also defined. Finally this paper briefly describes the application of this CIM-based information model by presenting a flow diagram about information integration and utilization between WAMS and SCADA.